





import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_MAPPING,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerModel,
    )
    from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import SegformerImageProcessor





class 					A__       (				__lowercase      ):




   """simple docstring"""



   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

          a__		:      List[Any]					=							self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase   ,    'hidden_sizes'))
          self.parent.assertTrue(hasattr(lowercase   ,    'num_attention_heads'))
          self.parent.assertTrue(hasattr(lowercase   ,    'num_encoder_blocks'))





class 					A__       :




   """simple docstring"""



   def __init__(   self   ,    lowercase   ,    lowercase=13   ,    lowercase=64   ,    lowercase=3   ,    lowercase=4   ,    lowercase=[2, 2, 2, 2]   ,    lowercase=[8, 4, 2, 1]   ,    lowercase=[16, 32, 64, 128]   ,    lowercase=[1, 4, 8, 16]   ,    lowercase=[1, 2, 4, 8]   ,    lowercase=True   ,    lowercase=True   ,    lowercase="gelu"   ,    lowercase=0.1   ,    lowercase=0.1   ,    lowercase=0.02   ,    lowercase=3   ,    lowercase=None   ,    )     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      Tuple					=							parent
          a__		:      Union[str, Any]					=							batch_size
          a__		:      List[Any]					=							image_size
          a__		:      List[Any]					=							num_channels
          a__		:      int					=							num_encoder_blocks
          a__		:      List[Any]					=							sr_ratios
          a__		:      int					=							depths
          a__		:      Any					=							hidden_sizes
          a__		:      Optional[Any]					=							downsampling_rates
          a__		:      Optional[int]					=							num_attention_heads
          a__		:      Union[str, Any]					=							is_training
          a__		:      Optional[Any]					=							use_labels
          a__		:      Dict					=							hidden_act
          a__		:      Optional[int]					=							hidden_dropout_prob
          a__		:      Optional[int]					=							attention_probs_dropout_prob
          a__		:      Any					=							initializer_range
          a__		:      Union[str, Any]					=							num_labels
          a__		:      List[str]					=							scope

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      Tuple					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__		:      List[str]					=							None
          if self.use_labels:
                 a__		:      str					=							ids_tensor([self.batch_size, self.image_size, self.image_size]   ,    self.num_labels)

          a__		:      str					=							self.get_config()
          return config, pixel_values, labels

   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          return SegformerConfig(
              image_size=self.image_size   ,    num_channels=self.num_channels   ,    num_encoder_blocks=self.num_encoder_blocks   ,    depths=self.depths   ,    hidden_sizes=self.hidden_sizes   ,    num_attention_heads=self.num_attention_heads   ,    hidden_act=self.hidden_act   ,    hidden_dropout_prob=self.hidden_dropout_prob   ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,    initializer_range=self.initializer_range   ,    )

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase)     ->		Tuple:







          '''simple docstring'''

          a__		:      str					=							SegformerModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__		:      int					=							model(lowercase)
          a__		:      str					=							self.image_size // (self.downsampling_rates[-1] * 2)
          self.parent.assertEqual(
              result.last_hidden_state.shape   ,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase)     ->		str:







          '''simple docstring'''

          a__		:      List[str]					=							self.num_labels
          a__		:      List[str]					=							SegformerForSemanticSegmentation(lowercase)
          model.to(lowercase)
          model.eval()
          a__		:      Any					=							model(lowercase)
          self.parent.assertEqual(
              result.logits.shape   ,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          a__		:      Any					=							model(lowercase   ,    labels=lowercase)
          self.parent.assertEqual(
              result.logits.shape   ,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          self.parent.assertGreater(result.loss   ,    0.0)

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase)     ->		int:







          '''simple docstring'''

          a__		:      int					=							1
          a__		:      Dict					=							SegformerForSemanticSegmentation(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__		:      int					=							torch.randint(0   ,    1   ,    (self.batch_size, self.image_size, self.image_size)).to(lowercase)
          a__		:      Union[str, Any]					=							model(lowercase   ,    labels=lowercase)
          self.parent.assertGreater(result.loss   ,    0.0)

   def 				__lowercase						(   self)     ->		Any:







          '''simple docstring'''

          a__		:      Optional[int]					=							self.prepare_config_and_inputs()
          a__		:      Union[str, Any]					=							config_and_inputs
          a__		:      str					=							{"pixel_values": pixel_values}
          return config, inputs_dict





@require_torch
class 					A__       (				__lowercase					,    __lowercase					,    unittest.TestCase      ):




   """simple docstring"""



   __A  :							Any			    =    (
       (
           SegformerModel,
           SegformerForSemanticSegmentation,
           SegformerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   __A  :							Dict			    =    (
       {
           '''feature-extraction''': SegformerModel,
           '''image-classification''': SegformerForImageClassification,
           '''image-segmentation''': SegformerForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __A  :							Tuple			    =    True
   __A  :							Optional[int]			    =    False
   __A  :							Dict			    =    False
   __A  :							Optional[Any]			    =    False

   def 				__lowercase						(   self)     ->		Optional[Any]:







          '''simple docstring'''

          a__		:      Any					=							SegformerModelTester(self)
          a__		:      Tuple					=							SegformerConfigTester(self   ,    config_class=lowercase)

   def 				__lowercase						(   self)     ->		List[str]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          a__		:      str					=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      List[Any]					=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase)

   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          a__		:      int					=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*lowercase)

   @unittest.skip('SegFormer does not use inputs_embeds')
   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          pass

   @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

          pass

   def 				__lowercase						(   self)     ->		List[str]:







          '''simple docstring'''

          a__		:      int					=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__		:      str					=							model_class(lowercase)
                 a__		:      Optional[int]					=							inspect.signature(model.forward)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a__		:      Optional[int]					=							[*signature.parameters.keys()]

                 a__		:      Union[str, Any]					=							["pixel_values"]
                 self.assertListEqual(arg_names[:1]   ,    lowercase)

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      Optional[Any]					=							self.model_tester.prepare_config_and_inputs_for_common()
          a__		:      Dict					=							True

          for model_class in self.all_model_classes:
                 a__		:      Optional[int]					=							True
                 a__		:      str					=							False
                 a__		:      int					=							True
                 a__		:      Union[str, Any]					=							model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__		:      Dict					=							model(**self._prepare_for_class(lowercase   ,    lowercase))
                 a__		:      Any					=							outputs.attentions

                 a__		:      int					=							sum(self.model_tester.depths)
                 self.assertEqual(len(lowercase)   ,    lowercase)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 a__		:      Optional[Any]					=							True
                 a__		:      Optional[Any]					=							model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__		:      List[Any]					=							model(**self._prepare_for_class(lowercase   ,    lowercase))
                 a__		:      int					=							outputs.attentions

                 self.assertEqual(len(lowercase)   ,    lowercase)

                 # verify the first attentions (first block, first layer)
                 a__		:      List[str]					=							(self.model_tester.image_size // 4) ** 2
                 a__		:      str					=							(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(attentions[0].shape[-3:])   ,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,    )

                 # verify the last attentions (last block, last layer)
                 a__		:      List[Any]					=							(self.model_tester.image_size // 32) ** 2
                 a__		:      Tuple					=							(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                 self.assertListEqual(
                     list(attentions[-1].shape[-3:])   ,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]   ,    )
                 a__		:      int					=							len(lowercase)

                 # Check attention is always last and order is fine
                 a__		:      str					=							True
                 a__		:      List[Any]					=							True
                 a__		:      Dict					=							model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__		:      Dict					=							model(**self._prepare_for_class(lowercase   ,    lowercase))

                 self.assertEqual(out_len + 1   ,    len(lowercase))

                 a__		:      Dict					=							outputs.attentions

                 self.assertEqual(len(lowercase)   ,    lowercase)
                 # verify the first attentions (first block, first layer)
                 a__		:      Optional[Any]					=							(self.model_tester.image_size // 4) ** 2
                 a__		:      Any					=							(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:])   ,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,    )

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          def check_hidden_states_output(lowercase   ,    lowercase   ,    lowercase):
                 a__		:      Union[str, Any]					=							model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 with torch.no_grad():
                        a__		:      Dict					=							model(**self._prepare_for_class(lowercase   ,    lowercase))

                 a__		:      Tuple					=							outputs.hidden_states

                 a__		:      Tuple					=							self.model_tester.num_encoder_blocks
                 self.assertEqual(len(lowercase)   ,    lowercase)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:])   ,    [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]   ,    )

          a__		:      str					=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__		:      Optional[int]					=							True
                 check_hidden_states_output(lowercase   ,    lowercase   ,    lowercase)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a__		:      List[Any]					=							True

                 check_hidden_states_output(lowercase   ,    lowercase   ,    lowercase)

   def 				__lowercase						(   self)     ->		Any:







          '''simple docstring'''

          if not self.model_tester.is_training:
                 return

          a__		:      Union[str, Any]					=							self.model_tester.prepare_config_and_inputs_for_common()
          a__		:      Any					=							True

          for model_class in self.all_model_classes:
                 if model_class in get_values(lowercase):
                        continue

                 a__		:      int					=							model_class(lowercase)
                 model.to(lowercase)
                 model.train()
                 a__		:      List[str]					=							self._prepare_for_class(lowercase   ,    lowercase   ,    return_labels=lowercase)
                 a__		:      Dict					=							model(**lowercase).loss
                 loss.backward()

   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          pass

   @slow
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__		:      Optional[int]					=							SegformerModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_      (						)    ->						Dict:
       a__		:      Union[str, Any]					=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
       return image





@require_torch
class 					A__       (				unittest.TestCase      ):




   """simple docstring"""



   @slow
   def 				__lowercase						(   self)     ->		List[str]:







          '''simple docstring'''

          a__		:      Optional[int]					=							SegformerImageProcessor(
              image_scale=(512, 512)   ,    keep_ratio=lowercase   ,    align=lowercase   ,    do_random_crop=lowercase)
          a__		:      Dict					=							SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__		:      str					=							prepare_img()
          a__		:      List[Any]					=							image_processor(images=lowercase   ,    return_tensors='pt')
          a__		:      Union[str, Any]					=							encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__		:      Optional[int]					=							model(lowercase)

          a__		:      Optional[Any]					=							torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape   ,    lowercase)

          a__		:      Tuple					=							torch.tensor(
              [
                  [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
                  [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
                  [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,    lowercase   ,    atol=1e-4))

   @slow
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      str					=							SegformerImageProcessor(
              image_scale=(512, 512)   ,    keep_ratio=lowercase   ,    align=lowercase   ,    do_random_crop=lowercase)
          a__		:      List[Any]					=							SegformerForSemanticSegmentation.from_pretrained(
              'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(lowercase)

          a__		:      str					=							prepare_img()
          a__		:      Any					=							image_processor(images=lowercase   ,    return_tensors='pt')
          a__		:      List[Any]					=							encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__		:      List[str]					=							model(lowercase)

          a__		:      Optional[int]					=							torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape   ,    lowercase)

          a__		:      Dict					=							torch.tensor(
              [
                  [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
                  [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
                  [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]   ,    lowercase   ,    atol=1e-1))

   @slow
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      Dict					=							SegformerImageProcessor(
              image_scale=(512, 512)   ,    keep_ratio=lowercase   ,    align=lowercase   ,    do_random_crop=lowercase)
          a__		:      int					=							SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__		:      Optional[Any]					=							prepare_img()
          a__		:      List[str]					=							image_processor(images=lowercase   ,    return_tensors='pt')
          a__		:      Union[str, Any]					=							encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__		:      List[str]					=							model(lowercase)

          a__		:      List[Any]					=							outputs.logits.detach().cpu()

          a__		:      Tuple					=							image_processor.post_process_semantic_segmentation(outputs=lowercase   ,    target_sizes=[(500, 300)])
          a__		:      Dict					=							torch.Size((500, 300))
          self.assertEqual(segmentation[0].shape   ,    lowercase)

          a__		:      str					=							image_processor.post_process_semantic_segmentation(outputs=lowercase)
          a__		:      Union[str, Any]					=							torch.Size((128, 128))
          self.assertEqual(segmentation[0].shape   ,    lowercase)




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase    :   Tuple						=			logging.getLogger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:Union[str, Any]=-1		)						->					List[str]:
								# in NER datasets, the last column is usually reserved for NER label
								__UpperCamelCase      :  str       =   label_idx







	def   _lowerCamelCase							(   self						:str	,  a						:Tuple	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Dict       =   mode.value
								__UpperCamelCase      :  Union[str, Any]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Any       =   1
								__UpperCamelCase      :  List[str]       =   []
								with open(a	,  encoding="utf-8"		) as f:
															__UpperCamelCase      :  Tuple       =   []
															__UpperCamelCase      :  Dict       =   []
															for line in f:
																						if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																													if words:
																																				examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																																				guid_index += 1
																																				__UpperCamelCase      :  Dict       =   []
																																				__UpperCamelCase      :  List[str]       =   []
																						else:
																													__UpperCamelCase      :  Union[str, Any]       =   line.split(" "		)
																													words.append(splits[0]		)
																													if len(a		) > 1:
																																				labels.append(splits[self.label_idx].replace("\n"	,  ""		)		)
																													else:
																																				# Examples could have no label for mode = "test"
																																				labels.append("O"		)
															if words:
																						examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
								return examples







	def   _lowerCamelCase							(   self						:int	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					Optional[Any]:
								__UpperCamelCase      :  Tuple       =   0
								for line in test_input_reader:
															if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																						writer.write(a		)
																						if not preds_list[example_id]:
																													example_id += 1
															elif preds_list[example_id]:
																						__UpperCamelCase      :  Union[str, Any]       =   line.split()[0] + " " + preds_list[example_id].pop(0		) + "\n"
																						writer.write(a		)
															else:
																						logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,  line.split()[0]		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  List[str]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  Any       =   ["O"] + labels
															return labels
								else:
															return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:List[str]		)						->					int:
								# in CONLL2003 dataset chunk column is second-to-last
								super().__init__(label_idx=-2		)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  Optional[Any]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  int       =   ["O"] + labels
															return labels
								else:
															return [
															    "O",
															    "B-ADVP",
															    "B-INTJ",
															    "B-LST",
															    "B-PRT",
															    "B-NP",
															    "B-SBAR",
															    "B-VP",
															    "B-ADJP",
															    "B-CONJP",
															    "B-PP",
															    "I-ADVP",
															    "I-INTJ",
															    "I-LST",
															    "I-PRT",
															    "I-NP",
															    "I-SBAR",
															    "I-VP",
															    "I-ADJP",
															    "I-CONJP",
															    "I-PP",
															]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, Any]	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Optional[Any]       =   mode.value
								__UpperCamelCase      :  List[str]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Dict       =   1
								__UpperCamelCase      :  List[str]       =   []

								with open(a	,  encoding="utf-8"		) as f:
															for sentence in parse_incr(a		):
																						__UpperCamelCase      :  Optional[int]       =   []
																						__UpperCamelCase      :  int       =   []
																						for token in sentence:
																													words.append(token["form"]		)
																													labels.append(token["upos"]		)
																						assert len(a		) == len(a		)
																						if words:
																													examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																													guid_index += 1
								return examples







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					str:
								__UpperCamelCase      :  List[Any]       =   0
								for sentence in parse_incr(a		):
															__UpperCamelCase      :  Tuple       =   preds_list[example_id]
															__UpperCamelCase      :  Dict       =   ""
															for token in sentence:
																						out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0		)}) '
															out += "\n"
															writer.write(a		)
															example_id += 1







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						return f.read().splitlines()
								else:
															return [
															    "ADJ",
															    "ADP",
															    "ADV",
															    "AUX",
															    "CCONJ",
															    "DET",
															    "INTJ",
															    "NOUN",
															    "NUM",
															    "PART",
															    "PRON",
															    "PROPN",
															    "PUNCT",
															    "SCONJ",
															    "SYM",
															    "VERB",
															    "X",
															]